In the past, large scale computing projects were limited to individuals and enterprises that owned large physical data centers with towering racks of computers. Distributed computing has allowed individuals and organizations to carry out intensive data collection and analysis procedures using one or more remote servers. Data scientists, in particular, are increasingly turning to various networked solutions to extract, transform, and store large amounts of data to be used in data analytics tools. A data scientist or engineer may, for example, build an extract, transform, load (ETL) process, such as an ETL pipeline, to extract data from one or more specified locations, transform the data to properly format the data for further querying and analysis, and load the data into one or more target databases.
While parallel processing tools, such as ETL pipelines, have proven to be powerful data processing tools for data scientists and engineers, they are difficult and time consuming to set up for many individuals and organizations, particularly those that are new to data science technologies. Architecting a new ETL pipeline may require an individual to navigate the configurations of multiple servers (e.g., application versions, operating system versions) and ensure their compatibilities with each other. Often, requirements for various required tools may conflict. The instant disclosure, therefore, identifies and addresses a need for systems and methods for building an ETL pipeline.